Benchmark: prepared lambda2

Script Preparation code:

var x = [];
let l = (x,i) => x[i]
for(i=0; i<1000; i++){
	x.push(i)
}

Tests:

lambda
let y = []; for(i=0; i<1000; i++){ y[i] = l(x,i) }
no lambda
let z = []; for(i=0; i<1000; i++){ z[i] = x[i] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lambda
no lambda

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain what's being tested in the provided benchmark.

**Benchmark Overview**

The test case measures the performance of two different approaches: using a lambda function (`let l = (x,i) => x[i];`) and not using a lambda function (`for(i=0; i<1000; i++){ty[i] = x[i]}`).

**Options Compared**

There are two options being compared:

1. **Using a lambda function**: This approach creates a closure by capturing the `x` array and the loop variable `i` in the lambda expression. The lambda function is then invoked in each iteration of the loop, accessing elements of the `x` array using the current value of `i`.
2. **Not using a lambda function**: In this approach, the loop iterates over the range of numbers from 0 to 999 (not 1000), and for each iteration, it assigns the corresponding element of the `x` array to an empty array `ty`. Note that this approach is slightly incorrect as it should iterate up to 1000.

**Pros and Cons**

**Using a lambda function:**

Pros:

* More concise and expressive code
* Can be more efficient because it avoids the need for explicit loops and assignments

Cons:

* Can lead to issues with variable capture and scoping, especially when using closures
* May not be as readable or maintainable due to its concise nature

**Not using a lambda function:**

Pros:

* More straightforward and easy to understand code
* Avoids potential issues with variable capture and scoping

Cons:

* Requires more boilerplate code (the explicit loop)
* Less efficient than the lambda function approach

**Library Used**

In both test cases, no specific library is used. However, it's worth noting that JavaScript engines may use various libraries or frameworks to optimize performance or implement features like garbage collection.

**Special JS Feature or Syntax**

The test case uses a feature called "arrow functions" (also known as lambda functions), which were introduced in ECMAScript 2015 (ES6). Arrow functions provide a concise way to define small, single-expression functions. They are denoted by the `=>` symbol and have a syntax similar to regular function expressions.

**Other Considerations**

When interpreting these results, consider that:

* The benchmark is running on Firefox 114, which may have specific optimizations or features enabled that affect performance.
* The `ExecutionsPerSecond` value indicates how many times each test case can be executed within one second. A higher value typically means better performance.

**Other Alternatives**

If you're interested in exploring alternative approaches, consider the following:

1. **Manual loop optimization**: Instead of using a lambda function or an array with push(), try optimizing the loop itself by reducing unnecessary allocations or iterations.
2. **Using `for...of` loops**: This syntax can provide better performance and readability than traditional `for` loops in some cases.
3. **Profiling and benchmarking tools**: Utilize built-in profiling tools (e.g., Chrome DevTools, Firefox Profiler) or third-party libraries to identify performance bottlenecks in your code.

Keep in mind that the specific options and alternatives may depend on the context of your project and the requirements you're trying to meet.

Related benchmarks:

prepared lambda2 (version: 0)

Comparing performance of: lambda vs no lambda

Created: 2 years ago by: Guest

Jump to the latest result

lambda

no lambda

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):